如何将GroupBy Count显示为分类数据的Bokeh vbar

2019年7月28日 260次阅读

我有一个小问题,在0.13.0中创建一个Bokeh vbar

来自数据帧组计数操作.响应
here是针对多层次组的,而不是我的.

自发布以来的更新

>根据提供的答案添加示例数据和代码,以查看问题是否是我的代码或其他内容

大纲

大熊猫数据框包含调查回复

>很棒
>好
>差
>满意
>很好

在列(‘ResponseID’,’RateGeneral’,’RateAccomodation’,’RateClean’,’RateServices’)和dtype被设置为catagory.我想使用显示响应计数组的散景vbar

DemoDFCount = DemoDF.groupby('RateGeneral').count()

我的散景代码看起来像这样

pTest= figure(title='Rating in General',plot_height=350)
pTest.vbar(width=0.9,source=DemoDFCount, x='RateGeneral',top='ResponseID')
show(pTest))

但不生成任何图表只有标题和工具栏

如果我使用pandas DemoDFCount.plot.bar(legend = False)我可以绘制一些内容,但如何在散景中创建此图表？

样本数据为json export

来自DemoDF.to_json()的50行样本数据

'{"ResponseID":{"0":1,"1":2,"2":3,"3":4,"4":5,"5":6,"6":7,"7":8,"8":9,"9":10,"10":11,"11":12,"12":13,"13":14,"14":15,"15":16,"16":17,"17":18,"18":19,"19":20,"20":21,"21":22,"22":23,"23":24,"24":25,"25":26,"26":27,"27":28,"28":29,"29":30,"30":31,"31":32,"32":33,"33":34,"34":35,"35":36,"36":37,"37":38,"38":39,"39":40,"40":41,"41":42,"42":43,"43":44,"44":45,"45":46,"46":47,"47":48,"48":49,"49":50},"RateGeneral":{"0":"Good","1":"Satisfactory","2":"Good","3":"Poor","4":"Good","5":"Satisfactory","6":"Excellent","7":"Good","8":"Good","9":"Satisfactory","10":"Satisfactory","11":"Excellent","12":"Satisfactory","13":"Excellent","14":"Satisfactory","15":"Very Good","16":"Satisfactory","17":"Excellent","18":"Very Good","19":"Excellent","20":"Satisfactory","21":"Good","22":"Satisfactory","23":"Excellent","24":"Satisfactory","25":"Good","26":"Excellent","27":"Very Good","28":"Good","29":"Very Good","30":"Good","31":"Satisfactory","32":"Very Good","33":"Very Good","34":"Very Good","35":"Good","36":"Excellent","37":"Satisfactory","38":"Excellent","39":"Good","40":"Good","41":"Satisfactory","42":"Very Good","43":"Very Good","44":"Poor","45":"Excellent","46":"Good","47":"Excellent","48":"Satisfactory","49":"Good"},"RateAccomodation":{"0":"Very Good","1":"Excellent","2":"Satisfactory","3":"Satisfactory","4":"Good","5":"Good","6":"Very Good","7":"Very Good","8":"Good","9":"Satisfactory","10":"Satisfactory","11":"Excellent","12":"Satisfactory","13":"Excellent","14":"Good","15":"Very Good","16":"Good","17":"Excellent","18":"Excellent","19":"Very Good","20":"Good","21":"Satisfactory","22":"Good","23":"Excellent","24":"Satisfactory","25":"Very Good","26":"Excellent","27":"Excellent","28":"Good","29":"Very Good","30":"Very Good","31":"Very Good","32":"Excellent","33":"Very Good","34":"Very Good","35":"Very Good","36":"Excellent","37":"Satisfactory","38":"Excellent","39":"Good","40":"Excellent","41":"Poor","42":"Very Good","43":"Very Good","44":"Poor","45":"Excellent","46":"Satisfactory","47":"Excellent","48":"Good","49":"Good"},"RateClean":{"0":"Excellent","1":"Excellent","2":"Satisfactory","3":"Good","4":"Excellent","5":"Very Good","6":"Very Good","7":"Excellent","8":"Excellent","9":"Satisfactory","10":"Satisfactory","11":"Excellent","12":"Good","13":"Good","14":"Excellent","15":"Excellent","16":"Good","17":"Excellent","18":"Excellent","19":"Excellent","20":"Good","21":"Very Good","22":"Poor","23":"Very Good","24":"Satisfactory","25":"Very Good","26":"Excellent","27":"Good","28":"Poor","29":"Good","30":"Excellent","31":"Good","32":"Good","33":"Very Good","34":"Satisfactory","35":"Good","36":"Excellent","37":"Satisfactory","38":"Excellent","39":"Good","40":"Very Good","41":"Satisfactory","42":"Excellent","43":"Excellent","44":"Very Good","45":"Excellent","46":"Good","47":"Excellent","48":"Good","49":"Excellent"},"RateServices":{"0":"Very Good","1":"Excellent","2":"Good","3":"Good","4":"Excellent","5":"Good","6":"Good","7":"Very Good","8":"Good","9":"Satisfactory","10":"Satisfactory","11":"Excellent","12":"Good","13":"Very Good","14":"Good","15":"Excellent","16":"Poor","17":"Excellent","18":"Excellent","19":"Excellent","20":"Good","21":"Good","22":"Very Good","23":"Excellent","24":"Satisfactory","25":"Very Good","26":"Excellent","27":"Very Good","28":"Good","29":"Excellent","30":"Very Good","31":"Excellent","32":"Good","33":"Excellent","34":"Very Good","35":"Very Good","36":"Excellent","37":"Satisfactory","38":"Excellent","39":"Good","40":"Very Good","41":"Satisfactory","42":"Excellent","43":"Excellent","44":"Good","45":"Excellent","46":"Very Good","47":"Excellent","48":"Good","49":"Very Good"}}'

最佳答案在另一个问题中它是多层次的事实并不是真正相关的.当您使用Pandas GroupBy作为Bokeh的数据源时,Bokeh使用group.describe的结果(其中包括每组每列的计数)作为数据源的内容.这是一个完整的例子,显示了来自“cars”数据集的Counts-per-Origin：

from bokeh.io import show, output_file
from bokeh.plotting import figure
from bokeh.sampledata.autompg import autompg as df

output_file("groupby.html")

df.origin = df.origin.astype(str)
group = df.groupby('origin')

p = figure(plot_height=350, x_range=group, title="Count by Origin",
           toolbar_location=None, tools="")

# using yr_count, but count for any column would work
p.vbar(x='origin', top='yr_count', width=0.8, source=group)

p.y_range.start = 0
p.xgrid.grid_line_color = None

show(p)